Glycotransferases are sugar polymer-building enzymes that build glycans in a non-template driven fashion. Aberrant glycotransferase activity creates abnormal sugar polymers (glycans) which are a hallmark of essentially every known cancer. Abnormal glycans appear to facilitate the ability of cancer cells to avoid innate immune detection, detach from native sites, and traverse non-native tissues, allowing the cancer cells to metastasize. Glycotransferase enzymes build at specific glycan polymer branch-points (chain link sites) and, as a general rule, exhibit strict donor, acceptor, and linkage specificity. With these ideas in mind we developed the idea that specific monosaccharide-and-linkage-specific glycan polymer chain links (glycan nodes, as we call them), if broken down, condensed and quantified from the pool of all glycan structures in a biological sample, could potentially serve a direct molecular surrogates of aberrant glycotransferase activity-in general contrast to traditiona glycomics approaches that look at whole, intact glycans. To enable this novel 'bottom-up' glycomics concept, an analytical methodology was devised and recently published that allows the simultaneous quantification of more than two dozen types of glycan nodes from N-, O-, and lipid-linked glycans relative to one another using only 10 microliters of blood serum or plasma. Samples from 3 independent lung cancer pilot study cohorts have now been studied, including 1) 31 stage IA lung adenocarcinoma patients and 31 individually paired age/gender/smoking matched controls from the NYU Lung Cancer Biomarker Center (an NCI EDRN site), 2) 30 newly diagnosed lung cancer patients and 29 age/gender/smoking status-matched controls from the INCO-COPERNICUS lung cancer in central and eastern Europe study, and 3) 24 lung cancer patients and 25 age/gender matched controls from a commercial biobank. Each cohort analyzed produced promising results with regard to distinguishing cases from controls; all were in general agreement about which glycan nodes are aberrantly produced in lung cancer patients. The goal of this project is to validate glycan node analysis as a tool for the early detection of lung cancer and as a classifier of lung tumor histology. Now that analytical and computational (data analysis) procedures for detection are locked down, this will be accomplished by carefully selecting and analyzing large sets of lung cancer patient samples, followed by well-defined biostatistical analysis for detection and multivariate model building for histology classification. Sample sets will include 1) an expanded set of 170 stage IA lung adenocarcinoma cases and 170 age/gender/smoking matched controls from the NYU Lung Cancer Biomarker Center, 2) 500 newly diagnosed cases and 500 controls from the INCO-COPERNICUS study, and 3) a similar set of 100 cases and 100 controls from ongoing studies at The University of Texas MD Anderson Cancer Center in Houston. This study will unambiguously define the role of blood-based glycan nodes as lung cancer biomarkers.